Learning an accurate image classification model requires large collections of manually annotated training examples. This requirement is even more stringent for deep learning methods, which have been shown as the state-of-the-art in multi-class image classification tasks. Moreover, when the same manual annotation process has to be repeated over and over again across multiple domains (e.g., different testing sites, different types of cameras, etc.), the substantial time and effort to manually annotate a large set of training examples for every instance can result in excessive operational cost and overhead. One important step in training such data hungry systems is to augment the data with various transformations such as cropping, flipping, etc., of the given labelled data. In some applications, however, the target domain where these models are applied have different characteristics than the given images and the augmented data. Hence, these transformations are not so useful and the performance in new domain is not satisfactory.
Domain adaptation of statistical classifiers is an important problem that arises when the data distribution in the new domain is different from that in the training domain. In many real-world classification problems, it is necessary to adapt the models to new domains. For example, binary classifiers for vehicle passenger detection trained with data from one site needs to have consistent performance across multiple sites. However, due to many uncontrollable reasons, such as the slight variations in setting up the image acquisition systems (e.g., camera angles with respect to the vehicles) or the inherent differences from site to site (e.g., traffic patterns with respect to the sun), the images collected from each site can vary greatly in terms of contrast, size of ROI's, and the locations of the human heads, etc. Hence, a classifier trained with images collected from one site often cannot achieve the same performance with images from another site. The classifier has to be either retrained from scratch or modified/fine-tuned to a new domain with only a few training examples from the new domain.
Recently, deep learning architectures have been shown to outperform all previous combination of handcrafted features and shallow classifiers for image classification tasks. However, deep learning approaches rely heavily on the amount of labeled images available. The effort and cost associated with manually labeling a large amount of images for training a CNN (Convolutional Neural Network) is impractical for many applications. Hence, there is a strong desire to investigate/develop approaches for domain adaptation, especially when only a small set of unlabeled images from the new domain is available.